The University of Southampton
Courses

# STAT6108 Analysis of Hierarchical (Multilevel & Longitudinal) Data

## Module Overview

This module introduces students to the main statistical modelling approaches that can handle hierarchical data structures. The module has mainly an applied scope where basic theory is introduced to ensure understanding. Practical computer sessions using MLwiN and R are conducted where appropriate.

### Aims and Objectives

#### Module Aims

The purpose of the model is to introduce students to the main statistical models for hierarchical and longitudinal data, with emphasis on multilevel and marginal models.

#### Learning Outcomes

##### Learning Outcomes

Having successfully completed this module you will be able to:

• Contrast the main statistical models available for the analysis of hierarchical and longitudinal data.
• Use multilevel and marginal models for the analysis of hierarchical and longitudinal data.
• Interpret the results from these statistical analyses in non-technical language.
• Understand the potential of more advanced elements, such as contextual variables and heterogeneous covariance structures for the analysis of hierarchical and longitudinal data.
• Appreciate the basic statistical theory underpinning multilevel and marginal models.

### Syllabus

• Hierarchical data structures • Multilevel models: random intercepts and random slopes; contextual variables, cross-level interactions and heterogeneous variance structures • Model building: estimation; testing; diagnostic checking (specification issues and residual analysis); model selection • Longitudinal data structures • Multilevel and Marginal models for longitudinal data • Growth curve models • Models for hierarchical and longitudinal binary response data The module will integrate the theory with practical application using MLwiN and R.

#### Special Features

This module combines theory and practice, via lectures and computer sessions.

### Learning and Teaching

#### Teaching and learning methods

This unit is taught via one double lecture per week and supported by practical computer sessions. Additional reading material and formative tests are provided where appropriate.

TypeHours
Independent Study75
Teaching25
Total study time100

#### Resources & Reading list

Snijders, T.A.B. and Bosker, R.J. (1999, 2012). Multilevel Analysis.

Goldstein, H. (2011). Multilevel Statistical Models.

Goldstein, H. (1995). Multilevel Statistical Models.

Laboratory space and equipment required. Students require access to computer lab with R and MLwiN.

Bryk, A.S. and Raudenbush, S.W. (1992, 2002). Hierarchical Linear Models: Applications and Data Analysis Methods.

Diggle, P. J., Liang, K-Y. and Zeger, S. L. (1994, 2001). The Analysis of Longitudinal Data.

Singer, Judith D., Willett, John B. (2003). Applied Longitudinal Data Analysis: Modelling Change andEvent Occurrence.

### Assessment

#### Summative

MethodPercentage contribution
Coursework  (4000 words) 100%

#### Referral

MethodPercentage contribution
Coursework  (4000 words) 100%

#### Repeat Information

Repeat type: Internal & External

### Linked modules

Prerequisites: STAT6083 or RESM6004 or RESM6104

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